Image analysis algorithms for cell contour recognition in budding yeast

Optics Express
Mats KvarnströmMikael Käll

Abstract

Quantification of protein abundance and subcellular localization dynamics from fluorescence microscopy images is of high contemporary interest in cell and molecular biology. For large-scale studies of cell populations and for time-lapse studies, such quantitative analysis can not be performed effectively without some kind of automated image analysis tool. Here, we present fast algorithms for automatic cell contour recognition in bright field images, optimized to the model organism budding yeast (Saccharomyces cerevisiae). The cell contours can be used to effectively quantify cell morphology parameters as well as protein abundance and subcellular localization from overlaid fluorescence data.

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Citations

Sep 19, 2014·Molecular Biology of the Cell·Anyimilehidi Mazo-VargasNicolas E Buchler
Sep 16, 2009·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Seungil HuhRobert F Murphy
Oct 2, 2015·BMC Bioinformatics·Tsuyoshi KatoKinya Okada
Mar 23, 2013·PloS One·Andreas DoncicJan M Skotheim
Jul 10, 2013·BioTechniques·L Louis DreyJan Bieschke
Dec 30, 2016·Molecular Biology of the Cell·Natalia CarbóPablo S Aguilar
Oct 3, 2017·Bioinformatics·Elco BakkerMatthew M Crane
Aug 19, 2015·Molecular and Cellular Biology·Alessandro RienzoMarkus Proft
Oct 21, 2011·Integrative Biology : Quantitative Biosciences From Nano to Macro·Elzbieta Petelenz-KurdzielMattias Goksör
Jul 30, 2017·International Journal for Numerical Methods in Biomedical Engineering·Ehab Essa, Xianghua Xie
Mar 15, 2014·The Journal of Biological Chemistry·Loubna BendriouaStefan Hohmann
Feb 10, 2017·Journal of the Royal Society, Interface·Cristian VersariGregory Batt
Jun 7, 2017·BMC Systems Biology·Niek WelkenhuysenStefan Hohmann
Nov 18, 2020·BMC Biology·Mikael MolinAnders Blomberg

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